Biometric identification systems are known. In these systems, an image is typically taken of some aspect of a person's physiology and information from the image is compared to stored data corresponding to that physiological aspect. The degree of correlation between the acquired image and the stored data determines whether the person corresponding to the acquired image is the person from which the stored data has been obtained. The stored data may correspond to a person's fingerprint, face, and/or voice. Each type of biometric possesses advantages and disadvantages. For example, fingerprints require contact with a person to obtain the image of the fingerprint for comparison to the stored data. Because contact with a person to be identified is not always possible, this form of identification may be problematic.
One reliable way of identifying persons at a distance has been identification of a person through an image of a human eye iris. The iris of a human eye possesses a pattern of high complexity that changes very little over the life of a person. Iris patterns are so unique that the iris patterns of the left and right eyes of the same person are different. Additionally, the iris patterns can be obtained at a distance using a near infrared (NIR) camera with an appropriate lens. The iris is protected by the cornea of an eye. The uniqueness and relatively minor changes in the iris under different environmental conditions makes the iris a good candidate for automated and highly reliable personal identification.
In previously known iris identification systems, such as the one disclosed in U.S. Pat. No. 5,291,560 to Daugman, an image of a person's eye is obtained and then processed to identify the portion of the eye that corresponds to the iris. Data from the iris that are not occluded by the eyelids may be used to generate a raw data signal. This signal may then be filtered using a pair of two-dimensional Gabor filters to extract pattern information from the raw data signal. The resulting data signal may be compared to stored data for identification purposes. In the Daugman reference, Hamming distances are selected to vary the criteria for evaluating an identification match.
The quality of the iris image that is used for identification evaluation considerably affects the accuracy of the system. Failures to detect imposters and false identification of imposters are more likely to occur with blurred iris images. Many factors affect the quality of an iris image. These factors include blurriness, resolution, image contrast, iris occlusion, and iris deformation. Blurriness, however, remains one of the most significant problems for iris image acquisition. Methods that have been used to evaluate the quality of an iris image have been adversely affected by occlusion of the iris that occurs from the eyelids covering a portion of the iris. Being able to assess the quality of an iris image would help ensure that an iris identification system is obtaining an iris image containing sufficient information for identifying a person.